For applications requiring massive parallel compute capability, such as deep learning frameworks for AI applications, our highly engineered GPU co-processing engines provide a field-proven hardware foundation.
Powerful Parallel GPU Co-Processing is the Cornerstone of Machine Learning
For applications requiring massive parallel compute capability, such as deep learning frameworks for AI applications, our highly engineered GPU co-processing engines provide a field-proven hardware foundation. Curtiss-Wright GPGPU co-processing engines leverage the latest NVIDIA Tensor Cores (for machine learning) technology and are a critical component of the high-performance embedded computing (HPEC) ecosystem that delivers data center capability at the tactical edge.
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Your One Stop for Video Capture and Graphics Processing Modules
Designing a graphics-intensive application isn't an easy feat. Curtiss-Wright graphics and video modules reduce costs, speed time-to-market, and reduce program risk by providing low-power, high-reliability, rugged SWaP-optimized solutions based on modular open systems approaches.
- Graphics controllers and video frame grabbers provide man-machine interfaces where graphics and sensor imagery must be combined. These modules are supported with OpenGL software interfaces and support a wide range of functions from simple graphics output, to multi-head, high-performance 3D rendering.
- For safety-critical applications, DO-254 certifiable graphics modules are available with artifact kits to ease your certification process.
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Reduce Cost, Risk, and Time to Market With COTS Hardware
Our broad selection of open-architecture, commercial off-the-shelf (COTS) rugged embedded computing solutions process data in real-time to support mission-critical functions. Field-proven, highly engineered, and manufactured to stringent quality standards, Curtiss-Wright’s COTS boards leverage our extensive experience and expertise to reduce your program cost, development time, and overall risk.
How Can I Teach My Machine to Learn?
This white paper examines supervised, unsupervised, and semi-supervised approaches to machine learning, as well as their accuracy and trade-offs.
